itself millions of times. It then
incorporated the feedback
on actions and outcomes to
develop more accurate predictions and new strategies.

Examples of machine
learning are beginning to
appear more in everyday contexts. For instance, x.ai, a New
York City-based artificial intelligence startup, provides a
virtual personal assistant for
scheduling appointments over
email and managing calendars.

To train the virtual assistants,
development team members
had the virtual assistants study
the email interactions between
people as they schedule meetings with one another so that
the technology could learn to
anticipate the human responses and see the choices
humans make. Although this
training didn’t produce a formal catalog of outcomes, the
idea is to help virtual assistants
mimic human judgment so
that over time, the feedback
can turn some aspects of
judgment into prediction
problems.

By breaking down tasks
into their constituent components, we can begin to see ways
AI will affect the workplace.

Although the discussion aboutAI is usually framed in termsof machines versus humans,we see it more in terms ofunderstanding the level ofjudgment necessary to pursueactions. In cases where wholedecisions can be clearly de-fined with an algorithm (forexample, image recognitionand autonomous driving),we expect to see computersreplace humans. This will takelonger in areas where judg-ment can’t be easily described,although as the cost of predic-tion falls, the number of suchtasks will decline.

Employing PredictionMachines

Major advances in prediction
may facilitate the automation
of entire tasks. This will require machines that can both
generate reliable predictions
and rely on those predictions
to determine what to do next.

For example, for many
business-related language
translation tasks, the role of
human judgment will become
limited as prediction-driven
translation improves (though
judgment might still be important when translations are
part of complex negotiations).

However, in other contexts,
cheaper and more readily
available predictions could
lead to increased value for
human-led judgment tasks.

For instance, Google’s
Inbox by Gmail can process
incoming email messages
and propose several short
responses, but it asks the
human judge which automated response is the most
appropriate. Selecting from a
list of choices is faster than
typing a reply, enabling the
user to respond to more
emails in less time.

Medicine is an area where AI
will likely play a larger role —
but humans will still have an
important role, too. Although
artificial intelligence can improve diagnosis, which is likely
to lead to more effective treatments and better patient care,
treatment and care will still rely
on human judgment. Different
patients have different needs,
which humans are better able
to respond to than machines.
There are many situations
where machines may never be
able to weigh the relevant pros
and cons of doing things one
way as opposed to another way
in a manner that is acceptable
to humans.

The ManagerialChallenge

As artificial intelligence technology improves, predictions
by machines will increasingly
take the place of predictions by
humans. As this scenario unfolds, what roles will humans
play that emphasize their
strengths in judgment while
recognizing their limitations in
prediction? Preparing for such
a future requires considering
three interrelated insights:

1. Prediction is not the
same as automation.
Prediction is an input in automation,
but successful automation
requires a variety of other activities. Tasks are made up of
data, prediction, judgment,

and action. Machine learning
involves just one component:
prediction. Automation also
requires that machines be involved with data collection,
judgment, and action. For example, autonomous driving
involves vision (data); scenarios — given sensory inputs,
what action would a human
take? (prediction); assessment
of consequences (judgment);
and acceleration, braking, and
steering (action). Medical care
can involve information about
the patient’s condition (data);
diagnostics (prediction);
treatment choices (judgment);
bedside manner (judgment
and action); and physical intervention (action). Prediction
is the aspect of automation in
which the technology is currently improving especially
rapidly, although sensor technology (data) and robotics
(action) are also advancing
quickly.

2. The most valuable
workforce skills involve judgment. In many work activities,
prediction has been the bottleneck to automation. In some
activities, such as driving, this
bottleneck has meant that
human workers have remained involved in prediction
tasks. Going forward, such
human involvement is all but
certain to diminish. Instead,
employers will want workers
to augment the value of

In cases where whole decisions can be clearlydefined with an algorithm, we expect to seecomputers replace humans.